2.2. Two variables, x and y, were measured for a random sample of 25 subjects, and two separate regression models werefit to the data. Least squares estimation of the parameters in Model A yielded the following equation and residual plot.log y 0.251+0.281 xResiduals Versus x(response is log y)log y 0.264+ 0.230 log xResiduals Versus log r(response is log y)Residual0.015 F0.075 f0.010+0.050-0.005+0.000Residual0.025-0.000--0.005+-0.025-0.010+-0.050--0.015+0.00.20.40.60.81.0-1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 -0.0log xLeast squares estimation of the parameters in Model B yielded the following equation and residual plot.Which of the following conclusions is correct?a) Model A is appropriate, since the relationship between x and y is linear.b) Model B is appropriate, since the relationship between x and y is linear.c) Model A is appropriate, since the relationship between log x and log y is linear.d) Model A is appropriate, since the relationship between log x and y is linear.e) Model B is appropriate, since the relationship between x and log y is linear.turay

In regression analysis, it's crucial to assess the adequacy of the model and the validity of its…

Answered: 2. 2. Two variables, x and y, were…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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4. Consider the following multiple regression results, where the dependent variable is the number of movie tickets sold per week, X, is the ticket price, and X2 is the cost of DVD rental. -51.0918X¡1 + 41.4607X¡2 (37.0184) (13.130) (0.584) Sp t = (-6.800) SSR = 17,023 SSE = 6,262 = 23,285 SST = п 3D 20 Complete the missing entries in the output. 1 | Are the slope coefficients, b, and b2, individually statistically significant (a = 0.10 2. 3. Calculate the standard error of the regression (s.) and the R2.
Q4. Shown below is a portion of a computer output for a regression analysis relating Y (dependent variable) and X (independent variable). ANOVA df SS Regression 1 115.064 Residual 13 82.936 Total Coefficients Standard Error Intercept 15.532 1.457 x -1.106 0.261 Perform a t test using the p-value approach and determine whether or not Y and X are related. Let a = 0.05. Formulate the null and alternative hypotheses Select the significance level Select the test statistic and calculate its value. Identify p-value/critical value for the test statistics and state the decision rule Compare calculated/p-value and critical/al[ha values and reach a conclusion about the null hypothesis
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 answer please : 1: Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.
The least-squares regression line relating two statistical variables is given as = 24 + 5x. Compute the residual if the actual (observed) value for y is 38 when x is 2. 4 38 2
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 83.3 + 2.24x, + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST 25.2 and SSR = 23.455. %D (a) Compute and interpret R² and R,. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R = 0.653 and R, = 0.595. Do you prefer the multiple regression results? Explain. %3D 2 Multiple regression analysi v ---Select--- ipreferred since both R2 and R, show ---Select--- O…
The Toyota Camry is one of the best-selling cars in North America. The cost of a previously owned Camry depends upon many factors, including the model year, mileage, and condition. To investigate the relationship between the car's mileage and the sales price for a 2007 model year Camry, the following data show the mileage and sale price for 19 sales (PriceHub website). Click on the datafile logo to reference the data. DATA file 1. 18 Price ($1000s) 16 14 12 10 18 6 If your answer is zero, enter "0". a. Select a scatter diagram with the car mileage on the horizontal axis and the price on the vertical axis. 20 40 6,0 8,0 100 Miles (1000s) 120 223 +♡ ♡ Miles(1000s) 29 36 47 63 77 73 87 92 101 110 28 59 68 68 91 42 65 110 Price ($1000s) 16.2 16.0 13.8 11.5 12.5 12.9 11.2 13.0 11.8 10.8 8.3 12.5 11.1 15.0 12.2 13.0 15.6 12.7 8.3
A student used multiple regression analysis to study how family spending (y) is influenced by income (x) family size (x2), and addition to savings(x3). The variables y, x1, and x3. The variables y, x1, and x3 are measured in thousands of dollars . The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficient Standard Error Intercept 0.0136 X1 0.7992 0.074 X2 0.2280 0.190 X3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant. Carry out a test to see if X3 and y are significantly related. Use a 5% level of significance
Suppose you are to estimate a simple regression for the following population model: Y=B₁ + B₁X + µl From a population of over thousands of observations, a small number of samples were randomly selected. The following is some of the information from the randomly selected sample.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.1 6.7 3.3 2.7 2.3 2.2 0.3 yy 13.8 10.7 9.9 6.6 6.1 5.9 6.1 4.5 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 6.4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Answer = Round to 3 decimal places.
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error Intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is…
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
The least-square regression line for the given data is y = 0.449x - 30.27. Determine the residual of a data point for which x = 98 and y = 15, rounding to three decimal places. Temperature, x Number of absences, y OA. 28.732 B. 1.268 C. 121.535 O D. 13.732 72 3 85 7 91 10 90 10 88 98 75 100 80 8 15 4 15 5

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